Health and Wealth of Elderly Couples: Causality Tests Using Dynamic Panel Data Models pot

IZA DP No. 1312 Health and Wealth of Elderly Couples: Causality Tests Using Dynamic Panel Data Models Pierre-Carl Michaud Arthur van Soest DISCUSSION PAPER SERIES Forschungsinstitut zur Zukunft der Arbeit Institute for the Study of Labor September 2004 Health and Wealth of Elderly Couples: Causality Tests Using Dynamic Panel Data Models Pierre-Carl Michaud CentER, Tilburg University and IZA Bonn Arthur van Soest RAND Corporation, Tilburg University and IZA Bonn Discussion Paper No. 1312 September 2004 IZA P.O. Box 7240 53072 Bonn Germany Phone: +49-228-3894-0 Fax: +49-228-3894-180 Email: iza@iza.org Any opinions expressed here are those of the author(s) and not those of the institute. Research disseminated by IZA may include views on policy, but the institute itself takes no institutional policy positions. The Institute for the Study of Labor (IZA) in Bonn is a local and virtual international research center and a place of communication between science, politics and business. 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IZA Discussion Paper No. 1312 September 2004 ABSTRACT Health and Wealth of Elderly Couples: Causality Tests Using Dynamic Panel Data Models ∗ A positive relationship between socio-economic status (SES) and health, the so-called "health-wealth gradient", is repeatedly found in most industrialized countries with similar levels of health care technology and economic welfare. This study analyzes causality from health to wealth (health causation) and from wealth to health (wealth or social causation) for elderly couples in the US. Using six biennial waves of couples aged 51-61 in 1992 from the Health and Retirement Study, we compare the recently developed strategy using Granger causality tests of Adams et al. (2003, Journal of Econometrics) with tests for causality in dynamic panel data models incorporating unobserved heterogeneity. While Adams et al. tests reject the hypothesis of no causality from wealth to husband's or wife's health, the tests in the dynamic panel data model do not provide evidence of wealth-health causality. On the other hand, both methodologies lead to strong evidence of causal effects from both spouses' health on household wealth. JEL Classification: C33, D31, I12, J14 Keywords: health, inequality, aging, dynamic panel data models, causality Corresponding author: Pierre-Carl Michaud Warandelaan 2 P.O. Box 90153 5000 LE Tilburg The Netherlands Email: p.c.michaud@uvt.nl ∗ We thank Jérome Adda, James Banks, Michael Haliassos, Michael Hurd, Arie Kapteyn, James Smith and Jonathan Temple for insightful discussions, seminar participants at RAND Santa Monica, Bristol, the RTN meeting in Edesheim, Tilburg and the 2004 Young Economist Meeting in Warsaw for comments. Part of this research was done while the first author was visiting the Institute for Fiscal Studies in London and the Labor and Population group/Center for the Study of Aging at RAND Corporation whose kind hospitality is gratefully acknowledged. 1 Introduction Explaining the health-wealth gradient, the observed association between wealth and health, has been a challenge for many economists as well as other social scientists. In the United States, respondents of the 1984 wave of the Panel Survey of Income Dynamics (PSID) who reported to be in excellent health had almost 75% higher median wealth than those who reported fair or poor health (Smith, 1999). Ten years later the ratio between median wealth of the same groups of respondents had grown to 274%, with median wealth $127,900 for those who reported excellent health in 1984, and $34,700 for those in fair or poor health in 1984 (amounts in 1996$). The ratio in 1984 was largest for the age group 45-54, an impressive 176%, which increased to 264% in 1994. Although often less pronounced than in the United States, a similar relation between socioeconomic status (SES) and health (the ”health-SES gradient”), is found in most industrialized countries with similar levels of health care technology and economic welfare (Wilkinson, 1996). Using data from the PSID, Deaton and Paxson (1998) show that the correlation between income and self-reported health increases over the life-cycle until about age 60 while the variance in self-reported health outcomes increases systematically over the life-cycle. Adda (2003) finds similar results for Sweden, with a health-wealth correlation that peaks at about the same age. In the United Kingdom, one of the puzzles created by the widely cited Whitehall I (1967) and II (1985-1988) studies (Marmot, 1999) looking at the health of civil servants over three decades, is that, among these individuals of similar socioeconomic status, the health-SES gradient, which was already substantial in 1967, has further increased over time, despite rising real median wealth and increasing efforts to facilitate access to health care (Smith, 1999). A similarly challenging finding is the evidence of Deaton and Paxson (1998) that, controlling for age, health assessments show no significant increases and even tend to decrease slightly for men and women born after 1945, even though, on average, these cohorts live longer and are wealthier than earlier cohorts. Understanding the sources of the gradient is important in order to understand the sources of health inequalities and to design economic policy measures to improve welfare, health and well-being. Curbing health inequalities may be desirable for many reasons. Deaton and Paxson (1998) argue that a mean-preserving spread in the health distribution leads to increasing mortality and reduced welfare under the plausible assumption that the marginal effect of health changes on mortality is higher at the bottom of the health distribution where individuals are more fragile and exposed to risks. Pradhan et al. (2003) argue that a social welfare function should have health as an argument and should be concave in that argument, if poor health is a stronger sign of deprivation of capabilities than income, in which case health becomes intrinsically important as opposed to instrumentally significant. Another reason why the gradient is important, is the relation between health, retire- ment, and incentives of social security benefits and health insurance. Health (measured from bad to good) is positively related to household savings, labor force participation, 2 and earnings, and negatively related to the social security retirement benefits replace- ment rate. Availability of Medicare at age 65 may explain the retirement peak at that age, where social security incentives no longer apply (Rust and Phelan, 1997; Blau and Gilleskie, 2001). Since the importance of public health insurance depends on health as well as SES, the health-SES relations are relevant for the debate on universal health care and the efficiency of proposed reforms. Attempts to understand the different causal effects (”pathways”) through which so- cioeconomic status and health affect each other have been numerous (see Smith, 1999 and Adler et al., 1994 for reviews). To understand the sources of the health-wealth or health-SES gradient, it is important to realize that health and wealth are dynamic pro- cesses that evolve over an individual’s life-cycle. A large part of the life-cycle is subject to the history of a series of shocks and events on the health and wealth front. Some of these are under the individual’s control and others are completely unpredictable. Pathways from health to wealth have been emphasized by economists, relying on the human capital theory by Grossman (1972), where health is seen as a stock that is built up through investment. 1 Health is worth investing in since it yields utility: it extends life and therefore the horizon over which gains from productivity can be used for consumption and provides consumption of healthy days that can be enjoyed through leisure (as opposed to sick days which do not yield utility). At a given point of the individual’s life-cycle, the health stock is the result of investments and shocks from the individual’s past, implying that as one progresses over the life-cycle, health is more and more predetermined by the complete past of the individual. The relation between health and wealth can be explained in this framework. Health and expectations about future health can affect productivity and hourly wages as well as labor supply at the intensive and the extensive margin. It therefore drives the ca- pacity to accumulate savings for retirement, and affects the retirement decision both in this way and through the direct effect of health on the marginal rate of substitution between leisure and work. Moreover, health affects expenditures directly, particularly in the United States where about 20% of workers below 65 are not covered by health insurance (Gruber, 1998), and where even those who are covered will often face copay- ments or additional expenditures such as prescription drugs not covered by Medicare. Consequently, health events can lead to considerable revisions of saving plans or other life-cycle decisions such as bequests (Smith, 2003). Causal effects from health to wealth are also referred to as health causation. 2 Pathways from wealth or more generally from socioeconomic status to health have been studied extensively in other social sciences (Adler et al., 1994) and since recently also in economics (Adams et al., 2003; Adda, 2003; Hurd and Kapteyn, 2003; Meer et al., 2003; Smith, 2003). This causal link is often named social causation which we will refer to as SES or wealth causation, the opposite of health causation. Theories explaining such a link have been put forward in various fields, such as biology, psychology, and economics. For example, one explanation is risk behaviors: the relation between behavior that is 1 see Dustmann and Windmeijer (1999) for an empirical application of the Grossman model. 2 This is often referred as health selection in the social science literature. 3 detrimental to health like smoking and drinking and socioeconomic status (Marmot, 1999). The effect of health on wealth may be related to access to health care. If not all people are fully covered by the same health insurance or if there are copayments or deductibles, those with low income or wealth will consume less health care services (in quantitative or qualitative terms) and thus invest less in their health. This cannot explain, however, why in the United Kingdom the wealth-health gradient has increased over a period in which general access to health care has increased, as shown by the two Whitehall studies. Moreover, it is hard to reconcile this explanation with the evidence provided by the RAND health experiment (Newhouse, 1993), which, in an experiment with randomly assigned copayment rates, showed that those with lower copayment rates used on average more health care services but did not experience significantly different health outcomes. The variation in quality of care and treatments that one can obtain in different so cioeconomic groups may be even more important to this issue than access to health care services per se. Indeed, the Grossman health production model implies that the marginal benefits of investment in health care can rise with education level (an indicator of socioeconomic status), explaining why the demand for health care quality increases with SES. Still, Kenkel (1991) finds that only part of the relationship between schooling and health is explained by real differences in health knowledge. Another potential causal effect of wealth on health through wealth inequalities comes from the stress associated with being at the bottom of the distribution (Wilkinson, 1996). In the Whitehall study, Marmot (1999) shows that there is some evidence that civil servants in higher ranks have lower level of cholesterol than those in lower ranks, suggesting that a low wealth position may create additional stress. The observation that wealth inequalities have risen but that the average health level may have fallen (as found by Deaton and Paxson, 1998) would be in line with this effect, as is Wilkinson’s (1996) finding that countries with higher wealth inequality tend to have higher mortality rates. A way to think of the effect of stress is to consider the adaptation of the health system to a series of stressful events. The immune system may adapt by functioning at a more intensive level, which may in the long run be detrimental to blood pressure and the health system. Episodes of stress such as the loss of a job may then in the long run lead to higher incidence of cardiovascular disease or high blood pressure. Since the frequency of stressful events differs across SES groups, allostatic loads, a measure of the cumulative effect of stressful events on the health system (see, e.g., Seeman et al., 1997), will be different across SES groups . A final set of explanations of the health-wealth gradient refers to early childhood. Small health events at the beginning of life may affect an individual’s complete health trajectory over the life-cycle (Barker, 1997). Following a sample of the March-1946 birth cohort in the UK over nearly 50 years, Wadsworth and Kuh (1997) found that early childhood events such as poor living conditions were significant predictors of many diseases later in life. Moreover, they showed that children of age two from this 1946 cohort had a higher risk of developing bronchitis if their parents had a similar childhood condition or smoked as adults, implying that health is partly transmitted from the 4 previous generation. Lindeboom et al. (2003) found that macroeconomic conditions at birth affected mortality hazards of cohorts throughout the 19th and 20th century, highlighting the importance (the “reach” in terms of Smith, 1999) of early childhood environment. Ravelli et al. (1998) showed that children born during the 1944-45 famine in Amsterdam were more likely to develop diabetes later in life. These examples show that health is partly determined by health of the parents or health in early childhood, which will be related to the parents’ SES due to the causal links from SES to health discussed above. Since there is also a strong intergenerational effect of SES, this can explain part of the health-SES gradient later in life. In our analysis of people aged fifty and over, such effects arise as permanent health shifts throughout the observation window. We will model them as individual specific health effects reflecting unobserved heterogeneity. Similar persistent unobserved heterogeneity terms may drive household wealth, and the unobserved heterogeneity terms in household wealth and in health of both spouses can be correlated. The goal of this pap er is to disentangle the sources of the health-wealth gradient: causal effects from health to wealth (health causation), causal effects from wealth to health (wealth or SES causation), observed exogenous factors that affect health and wealth in the same way, and correlated unobserved factors (unobserved heterogeneity) driving health as well as wealth. Panel data with extensive information on wealth and health offer a non-experimental setting in which causality can be addressed using common time series concepts of non-causality and conditional independence (Granger, 1969; Sims, 1972). If correlation between unobservables plays a role, these tests will only be valid if they control for such correlations (Chamberlain, 1984). Using Granger causality to study the health-wealth gradient was proposed by Adams et al. (2003), who test for an effect of wealth on health in the AHEAD cohort of age 70 and older. They only have three waves, limiting the richness of the dynamic specifications they can use. Moreover, they do not control for unobserved heterogeneity. Their results indicate a clear health causation channel but they also find some evidence of wealth/SES causation. They point out that rejecting their hypothesis of no Granger causality could also be an indication of correlated unobserved heterogeneity in health and wealth. Adda (2003) uses Swedish panel data for individuals over the whole life-cycle and implements a test for health and SES causation. He concludes that both causation mechanisms are present. He does not discuss or control for unobserved heterogeneity. On the other hand, Smith (2003) and Wu (2003) perform tests of health causation conditional on initial conditions. Since the initial values are correlated to the unob- servable heterogeneity terms, this goes in the direction of controlling for unobservables. They estimate the impact of onsets of critical health conditions such as cancer or lung disease on changes in wealth and other SES indicators, conditioning on initial health status. Smith (2003) looks at changes between the first and the fifth wave of the HRS, while Wu (2003) looks at changes over the first two waves. Using onsets as exogenous health shocks that are not affected by wealth changes seems a plausible identification strategy. Smith (2003) estimates that the cumulative effect of the onset of a critical disease after eight years is about $40,000, while Wu (2003) concludes that household 5 wealth responds more strongly to the onset of a serious condition for the wife than for the husband. Neither Smith (2003) nor Wu (2003) exploit the full panel nature of the HRS, implying that the dynamics of health and SES causation are not explored. Using a similar strategy to test for causal wealth-health effects, Meer et al. (2003) use three 5-year spaced observations from the PSID, using bequests as instruments that directly affect wealth but not health. Their test looks at the effect of wealth changes on self-reported health and the dynamics of their model imply that wealth changes have a one shot effect on health after which health returns to a stationary value. They find small and insignificant wealth-health effects. Adams et al. (2003) reject the hypothesis that wealth changes do not cause health changes for three of the four main causes of death among older men, as well as for self-reported general health status and for mental health. The latter results for the U.S. are also found by Adda et al. (2003) for the U.K. and Sweden. Using roughly similar models as Adams et al. (2003), Hurd and Kapteyn (2003) find that changes in health are more related to income in the U.S. than in the Netherlands. In all these three studies, a test of non-causality is performed without controlling for unobserved heterogeneity. As argued above, correlated unobserved heterogeneity may be important, because of genetic transmissions and early childhood effects and other persistent shocks on health as well as wealth. Not allowing for unobserved heterogeneity may bias the estimates and the test results, possibly explaining why the null of no causality is often rejected. In this paper, we develop a dynamic vector autoregressive panel data framework that makes it possible to test for health and wealth causation, controlling for unobserved heterogeneity. Alonso-Borrego and Arellano (1999) emphasize that dynamic vector au- toregressive panel data models offer a rich environment for performing such tests. We apply the framework to the HRS cohort of elderly households born between 1931 and 1941 who are observed over six biennial waves from 1992 to 2002. We consider health for each spouse but wealth at the household level, analyzing the interplay of health and wealth for elderly couples (as in Wu, 2003). We use the instruments of Smith (2003), Wu (2003) and Meer et al. (2003) to identify the structural links between health and wealth, conditioning on initial conditions. We perform the tests and explore their sen- sitivity to different sets of assumptions, particularly concerning the types of dynamic feedback allowed for and the specification of the initial conditions (Ahn and Schmidt, 1995; Blundell and Bond, 1998). We also present some results where we separately look at mental and physical health, distinguish between couples that do and do not have access to health insurance, and look at liquid and non-liquid wealth. The paper is organized as follows. In section 2 we document the association between wealth and health and the way it evolves over time for the HRS cohort. In section 3, the econometric framework is presented and the identification, testing and estimation strategies are discussed. Section 4 presents the results of the Adams et al. (2003) tests and Section 5 presents the results for the dynamic panel data models. Section 6 concludes. Some more detailled results can be found in appendices at the end of the paper. 6 2 Wealth and Health in the HRS cohort The Health and Retirement Study is a longitudinal survey of individuals aged 51-61 in 1992 in the United States. The project started in 1990 and was funded by the National Institute on Aging and other partners such as the Social Security Administration. Data were collected every two years and cover a wide range of aspects of the life of elderly singles and couples. For the first wave of 1992, 12,652 interviews were conducted for a random sample of individuals born 1931-1941. Spouses of these individuals were also included in the sample even if they were not eligible according to their age. We use the public release file from the RAND corporation that merged records from the six available waves (1992-2002). 3 Data is arranged by couples consisting of respon- dent and spouse. We select all couples present in 1992 with complete information on the relevant variables during their participation in the HRS. To avoid losing too many observations, we retain observations with missing information or bracket information on one or more components of wealth, using imputed values (see below). We observe couples until one of the spouses dies, until the dissolution of the household because of divorce or separation, or until one member of the household is not interviewed. We do not analyze widows and widowers or divorced or separated spouses, since we focus on the interplay between wealth and the health of the two spouses in a couple. Table 1 gives the frequencies at each wave along with the recorded exits from our sample. Overall, the average attrition rate for each wave is roughly 10% which gives an annual attrition rate of about 5%. 4 In 1992, there are 4,160 households of which 2,463 remain until the sixth wave in 2002. [Table 1 about here] Table 2 shows the demographic composition of the sample in 1992 according to the number of waves the respondents remain in the panel. Wives are on average four years younger than their husband. Both spouses have a similar average level of education. Approximately 6% of respondents are Hispanic and about 8% are blacks. These figures reflect the oversampling of those groups in the HRS. About 8% of husbands are immi- grants, compared to 10% of the wives. One out of four respondent has been married at least once before their current relationship. Those who exit before the end of the panel are on average older, which is an obvious consequence of decreasing survival probabilities. Attritors have slightly less education than respondents who remain in the panel for all six waves. Blacks and Hispanics seem more likely to exit than others. [Table 2 about here] 3 See http://www.rand.org/labor/aging/dataprod/. We used version D of the data released in Jan- uary 2004. 4 From life-table figures, yearly death rates for this cohort vary from 0.5% to 2.6% over the decade considered (Berkeley Mortality Database: http://www.demog.berkeley.edu/wilmoth/mortality/ ). 7 2.1 Wealth Data We summarize wealth in two broad categories: liquid and non-liquid wealth. Liquid wealth consists of individual retirement accounts, stocks, bonds, certificate deposits, T- bills/saving bonds, checking/saving accounts and other debts and savings. Non-liquid wealth includes the net value of the primary residence, other real estate, and vehicles. This definition is the same as the one used by Adams et al. (2003), except that we do not include business assets, which is nonzero for not many respondents but varies enormously over time for some respondents. It does not include the value of defined contribution pension plans but does include the value of life insurances and other annuities (in ”other savings”). All amounts are expressed in US dollars of 2002 using the Consumer Price Index of the Bureau of Labor Statistics. In the analysis, we will use log transformations of the different wealth measures to reduce the effect of outliers. 5 Table 3 gives the composition of wealth holdings for our sample. The first column gives the percentage of cases where imputation was used across all waves for each wealth component. RAND Imputations are used for open and closed bracket responses and for ownership of specific items (see Hoynes et al. (1998) for a comparison of imputation methods). The next two columns give the median of each component conditional on ownership (with positive amount) and the ownership rates for the 1992 and 2002 waves. In 1992, respondents held more than two thirds of their wealth in non-liquid assets, primarily consisting of the value of the primary residence. The share of non-liquid assets in total wealth falls over the decade. Participation of the elderly in stocks and individual retirement accounts is far more important in the United States than in many other countries (Hurd, 2001). More than half of the respondents in the panel own Individual Retirement Accounts (IRAs), with a median value of $31,570 in 1992. Moreover, by 2002, 37.4% of households hold stocks for a median value of $50,000. Increases in IRA and stock holdings from 1992 to 2002 certainly reflect to some extent the high returns observed throughout the period. Par- ticipation went from 32.1% to 37.4% for stocks and from 45.1% to 47.2% for IRAs from 1992 to 2002. The median value of stocks and IRAs more than doubled over the 10 years. [Table 3 about here] 2.2 Health Variables Table 4 summarizes the health information for the 1992 and 2002 waves. The HRS age groups are subject to considerable health risks. In 1992, 16.7% (23.8%) of wives (husbands) have suffered from a condition that Smith (2003) labels a severe one: cancer, heart condition, lung disease or a stroke (or a combination of these). More than half the respondents have ever had an onset of a mild condition - diabetes, high blood 5 To deal with zero wealth (0.5-1% of the observations per wave) and negative wealth (2-3% of the observations per wave), we use the following log transformation: log(y) = 1(y ≥ 0) log(1 + y) −(1 − 1(y ≥ 0))(1 − log(−y)); For positive values of wealth, this is approximately log wealth. 8 [...]... evidence of causal effects from wealth on the wife’s health or from the husband’s health on the wife’s health 5.3 Some Disaggregated Results We have found clear evidence of causal effects of both the husband’s and the wife’s health on household wealth, using the constructed health index which incorporates all features of health Table 13 shows the results of a similar dynamic panel data model using separate... results of causality tests conditioning on unobserved heterogeneity will show whether controlling for unobserved heterogeneity is important 15 4.1 Wealth to Health To test the null hypothesis that wealth does not cause health, Tables 7 and 8 present the results of models that explain each indicator of health of husband and wife from lagged husband’s and wife’s health, lagged log wealth, and additional... log transformed value of household wealth As explained in section 1, a model explaining the evolution of wealth and health should have several features First, it must allow for instantaneous causality as well as dynamic feedback from wealth to health and vice versa This captures the most cited pathways, causal effects of wealth on health (wealth causation) and of health on wealth (health causation) Second,... from health to wealth and from wealth to health, our dynamic panel data model based tests also provide clear evidence of causal effects from health to wealth, but hardly any evidence of causal effects from wealth to either the husband’s or the wife’s health An analysis of the residuals suggests that this difference is not due to unobserved heterogeneity in 20 health, but to unobserved heterogeneity in wealth. .. waves of the HRS and finds that the wealth of households tends to respond more to health events of the wife than to health events of the husband A longer time span is needed to find the effect of the husband’s health [Insert Table 10 about here] 5.2 Wealth to Health The results for the equation explaining the husband’s health are presented in Table 11 Adding the second order lags and the interaction of lagged... negative effect of both lagged husband’s health and lagged wife’s health on log wealth Joint tests indicate that lagged values of husband’s health significantly affect log wealth, rejecting the hypothesis that husband’s health causes does not cause wealth This is the same conclusion as from the Adams et al (2003) tests in the previous section, but now unobserved heterogeneity is controlled for and the lag... 19 The results in Table 13 show evidence of causal household wealth effects of physical health for the husband and mental health for the wife Mental health is not significant for the husband and physical health is insignificant for wives A mental health problem of the wife has an instantaneous effect on household wealth, while the effect of the husband’s physical health is not instantaneous, in line with... spouse’s health In four out of eight cases, we find a positive and significant effect of the wife’s health on the husband’s health In the other four cases, the effect is insignificant The effect of the husband’s health on the wife’s health is significantly positive in five out of eight cases Focusing on the constructed health index, we find evidence of causality in both directions [Insert Tables 6 and 7 about... them and has retired before the last wave One of the potential channels of health- wealth causality is through labor supply and earnings, making it worthwhile to extend the model with labor supply (and the decision to retire) and earnings References [1] Adams, P., Hurd, M.D., McFadden, D., Merrill, A and T Ribiero (2003): Healthy, Wealthy and Wise? Tests for Direct Causal Paths between Health and Socioeconomic... [10] Blau, D.M and D Gilleskie (2001): Health Insurance and Retirement of Married Couples, Mimeo, University of North Carolina at Chapel Hill, October [11] Blundell, R .and S Bond (1998): Initial Conditions and Moment Restrictions in Dynamic Panel Data Models, Journal of Econometrics, 87, pp 115-143 [12] Chamberlain, G (1984): Panel Data, in Handbook of Econometrics, Vol 2, eds Z Griliches and M.D Intriligator, . Arbeit Institute for the Study of Labor September 2004 Health and Wealth of Elderly Couples: Causality Tests Using Dynamic Panel Data Models Pierre-Carl. IZA DP No. 1312 Health and Wealth of Elderly Couples: Causality Tests Using Dynamic Panel Data Models Pierre-Carl Michaud Arthur van